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Record W2939432331 · doi:10.1139/tcsme-2018-0230

Numerical simulation of an over-expanded supersonic and subsonic industrial nozzle flow relevant to flaring system

2019· article· en· W2939432331 on OpenAlexafffundvenue
Faisal Al Qurooni, Ali Vakil, Ehab Elsaadawy, Sheldon Green

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsCoanda Research and Development Corporation (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaSaudi Aramco
KeywordsNozzleSupersonic speedMechanicsTurbulenceMach numberJet (fluid)Compressible flowDischarge coefficientTurbulence kinetic energyTuyerePhysicsCombustionChoked flowFlow (mathematics)Materials scienceCompressibilityThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Flaring in oil and gas production is the controlled burning of unwanted exhaust gases to enhance safety. To improve flare combustion, gas flares are equipped with air nozzles that introduce extra oxygen and improve mixing in the combustion zone. These nozzles are operated in the subsonic, sonic, or supersonic regimes. In this paper, we are concerned with turbulence modeling of the jet flow exiting from a particular convergent–divergent nozzle used in flare systems. That nozzle has convergent and divergent sections that are connected via a throat section with a finite length and constant diameter. The Realizable k – ε and SST k – ω models are used to study the compressible flow within the nozzle. The velocity profiles, turbulent kinetic energy, Mach number profiles, and entrainment rate coefficients predicted by both turbulence models are compared for nozzle pressure ratios in the range 1.18 ≤ NPR ≤ 1.78. It is shown that both turbulence models predict nearly identical flow evolution along the nozzle. When the flow becomes supersonic, the shock surface, and consequently nozzle outlet velocity profiles, predicted by the SST k – ω model deviates slightly from the other model. The differences, however, become negligible a couple of diameters downstream of the nozzle outlet.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.208
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes3
Has abstractyes

Explore more

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